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A model of brain circulation and metabolism: NIRS signal changes during physiological challenges.

Banaji M, Mallet A, Elwell CE, Nicholls P, Cooper CE - PLoS Comput. Biol. (2008)

Bottom Line: We anticipate that the model will play an important role in helping to understand the NIRS signals, in particular, the cytochrome signal, which has been hard to interpret.A range of model simulations are presented, and model outputs are compared to published data obtained from both in vivo and in vitro settings.The comparisons are encouraging, showing that the model is able to reproduce observed behaviour in response to various stimuli.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Sciences, University of Essex, Colchester, United Kingdom. m.banaji@ucl.ac.uk

ABSTRACT
We construct a model of brain circulation and energy metabolism. The model is designed to explain experimental data and predict the response of the circulation and metabolism to a variety of stimuli, in particular, changes in arterial blood pressure, CO(2) levels, O(2) levels, and functional activation. Significant model outputs are predictions about blood flow, metabolic rate, and quantities measurable noninvasively using near-infrared spectroscopy (NIRS), including cerebral blood volume and oxygenation and the redox state of the Cu(A) centre in cytochrome c oxidase. These quantities are now frequently measured in clinical settings; however the relationship between the measurements and the underlying physiological events is in general complex. We anticipate that the model will play an important role in helping to understand the NIRS signals, in particular, the cytochrome signal, which has been hard to interpret. A range of model simulations are presented, and model outputs are compared to published data obtained from both in vivo and in vitro settings. The comparisons are encouraging, showing that the model is able to reproduce observed behaviour in response to various stimuli.

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Model response of TOS and ΔoxCCO to a step down in arterial                            oxygen saturation.(A) Response of TOS (percent). (B) Response of ΔoxCCO                                (μM). A hyperaemic effect is seen in both                            signals.
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pcbi-1000212-g011: Model response of TOS and ΔoxCCO to a step down in arterial oxygen saturation.(A) Response of TOS (percent). (B) Response of ΔoxCCO (μM). A hyperaemic effect is seen in both signals.

Mentions: The dynamic and steady state responses of modelled NIRS signals to hypoxia were explored. In the first simulation a one minute drop in arterial oxygen saturation from 96 percent to 80 percent was carried out. The results are plotted in Figure 11. Following hypoxia there is an increase in blood flow leading to a partial restoration of TOS (and to a lesser extent ΔoxCCO) during the hypoxia. This behaviour is connected with the rapidity of the drop in arterial oxygen saturation and so in simulations of real hypoxias (see next section) this adaptation is unlikely to be observed. Both TOS and ΔoxCCO show an overshoot associated with the hyperaemia following reoxygenation, consistent with some experimental observations [65].


A model of brain circulation and metabolism: NIRS signal changes during physiological challenges.

Banaji M, Mallet A, Elwell CE, Nicholls P, Cooper CE - PLoS Comput. Biol. (2008)

Model response of TOS and ΔoxCCO to a step down in arterial                            oxygen saturation.(A) Response of TOS (percent). (B) Response of ΔoxCCO                                (μM). A hyperaemic effect is seen in both                            signals.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2573000&req=5

pcbi-1000212-g011: Model response of TOS and ΔoxCCO to a step down in arterial oxygen saturation.(A) Response of TOS (percent). (B) Response of ΔoxCCO (μM). A hyperaemic effect is seen in both signals.
Mentions: The dynamic and steady state responses of modelled NIRS signals to hypoxia were explored. In the first simulation a one minute drop in arterial oxygen saturation from 96 percent to 80 percent was carried out. The results are plotted in Figure 11. Following hypoxia there is an increase in blood flow leading to a partial restoration of TOS (and to a lesser extent ΔoxCCO) during the hypoxia. This behaviour is connected with the rapidity of the drop in arterial oxygen saturation and so in simulations of real hypoxias (see next section) this adaptation is unlikely to be observed. Both TOS and ΔoxCCO show an overshoot associated with the hyperaemia following reoxygenation, consistent with some experimental observations [65].

Bottom Line: We anticipate that the model will play an important role in helping to understand the NIRS signals, in particular, the cytochrome signal, which has been hard to interpret.A range of model simulations are presented, and model outputs are compared to published data obtained from both in vivo and in vitro settings.The comparisons are encouraging, showing that the model is able to reproduce observed behaviour in response to various stimuli.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Sciences, University of Essex, Colchester, United Kingdom. m.banaji@ucl.ac.uk

ABSTRACT
We construct a model of brain circulation and energy metabolism. The model is designed to explain experimental data and predict the response of the circulation and metabolism to a variety of stimuli, in particular, changes in arterial blood pressure, CO(2) levels, O(2) levels, and functional activation. Significant model outputs are predictions about blood flow, metabolic rate, and quantities measurable noninvasively using near-infrared spectroscopy (NIRS), including cerebral blood volume and oxygenation and the redox state of the Cu(A) centre in cytochrome c oxidase. These quantities are now frequently measured in clinical settings; however the relationship between the measurements and the underlying physiological events is in general complex. We anticipate that the model will play an important role in helping to understand the NIRS signals, in particular, the cytochrome signal, which has been hard to interpret. A range of model simulations are presented, and model outputs are compared to published data obtained from both in vivo and in vitro settings. The comparisons are encouraging, showing that the model is able to reproduce observed behaviour in response to various stimuli.

Show MeSH